Current Topics in Medicinal Chemistry - Volume 22, Issue 20, 2022

Background: Although water is regarded as a simple molecule, its ability to create hydrogen bonds makes it a highly complex molecule that is crucial to molecular biology. Water molecules are extremely small and are made up of two different types of atoms, each of which plays a particular role in biological processes. Despite substantial research, understanding the hydration chemistry of protein-ligand complexes remains difficult. Researchers are working on harnessing water molecules to solve unsolved challenges due to the development of computer technologies. Objectives: The goal of this review is to highlight the relevance of water molecules in protein environments, as well as to demonstrate how the lack of well-resolved crystal structures of proteins functions as a bottleneck in developing molecules that target critical therapeutic targets. In addition, the purpose of this article is to provide a common platform for researchers to consider numerous aspects connected to water molecules. Conclusion: Considering structure-based drug design, this review will make readers aware of the different aspects related to water molecules. It will provide an amalgamation of information related to the protein environment, linking the thermodynamic fingerprints of water with key therapeutic targets. It also demonstrates that a large number of computational tools are available to study the water network chemistry with the surrounding protein environment. It also emphasizes the need for computational methods in addressing gaps left by a poorly resolved crystallized protein structure.

Breast cancer represents a health concern worldwide for being the leading cause of cancer- related women's death. The main challenge for breast cancer treatment involves its heterogeneous nature with distinct clinical outcomes. It is clinically categorized into five subtypes: luminal A; luminal B, HER2-positive, luminal-HER, and triple-negative. Despite the significant advances in the past decades, critical issues involving the development of efficient target-specific therapies and overcoming treatment resistance still need to be better addressed. OMICs-based strategies have marked a revolution in cancer biology comprehension in the past two decades. It is a consensus that Next-Generation Sequencing (NGS) is the primary source of this revolution and the development of relevant consortia translating pharmacogenomics into clinical practice. Still, new approaches, such as CRISPR editing and epigenomic sequencing are essential for target and biomarker discoveries. Here, we discuss genomics and epigenomics techniques, how they have been applied in clinical management and to improve therapeutic strategies in breast cancer, as well as the pharmacogenomics translation into the current and upcoming clinical routine.

Bruton’s tyrosine kinase (BTK) plays a vital role in B-cell antigen receptor (BCR) signalling transduction pathway. Controlling BCR signalling by BTK inhibitors is a promising therapeutic approach for the treatment of inflammatory and autoimmune diseases. Since the approval of ibrutinib for the treatment of different haematological cancers in 2013, great efforts have been made to explore new BTK inhibitors. Despite the remarkable potency and efficacy of first and second generation irreversible BTK inhibitors against various lymphomas and leukaemia, there are also some clinical limitations, such as off-target toxicity and primary/acquired drug resistance. Acquired drug resistance due to the C481S mutation in BTK is the major challenging problem of irreversible inhibitors. After, the BTK C481S mutation, the irreversible covalent inhibitors cannot form covalent bond with BTK and drop activities. Hence, there is an urgent need to develop novel BTK inhibitors to overcome the mutation problem. In recent years, a few reversible BTK inhibitors have been developed and are under clinical evaluation stages. In addition, a few reversible BTK-PROTACs have been explored and under developments. A number of reversible non-covalent BTK inhibitors, including MK1026/ ARQ531, LOXO305, fenebrutinib are at different stages of clinical trials for autoimmune diseases. In this review, we summarized the discovery and development of nextgeneration BTK inhibitors, especially targeting BTK C481S mutation and their applications for the treatment of lymphomas and autoimmune diseases.

Background: The lengthy and expensive process of developing a novel medicine often takes many years and entails a significant financial burden due to its poor success rate. Furthermore, the processing and analysis of quickly expanding massive data necessitate the use of cutting-edge methodologies. As a result, Artificial Intelligence-driven methods that have been shown to improve the efficiency and accuracy of drug discovery have grown in favor. Objective: The goal of this thorough analysis is to provide an overview of the drug discovery and development timeline, various approaches to drug design, and the use of Artificial Intelligence in many aspects of drug discovery. Methods: Traditional drug development approaches and their disadvantages have been explored in this paper, followed by an introduction to AI-based technology. Also, advanced methods used in Machine Learning and Deep Learning are examined in detail. A few examples of big data research that has transformed the field of medication discovery have also been presented. Also covered are the many databases, toolkits, and software available for constructing Artificial Intelligence/Machine Learning models, as well as some standard model evaluation parameters. Finally, recent advances and uses of Machine Learning and Deep Learning in drug discovery are thoroughly examined, along with their limitations and future potential. Conclusion: Artificial Intelligence-based technologies enhance decision-making by utilizing the abundantly available high-quality data, thereby reducing the time and cost involved in the process. We anticipate that this review would be useful to researchers interested in Artificial Intelligencebased drug development.